Japan, US, Germany, Netherlands, Switzerland, UK, Denmark, Finland, Iceland, Norway, Sweden, Australia, Canada, New Zealand, Israel.
Cluster 2: Followers:
Honk Kong (↑), South Korea (↑), Singapore (↑), Malaysia, Philippines, Thailand, Fiji, Austria (↑), Belgium (↑), France (↑), Luxembourg, Cyprus, Greece, Ireland, Italy, Malta, Portugal, Spain, Turkey, Bahrain, Jordan, Kuwait, Lebanon, Saudi Arabia, Syria, United Arab Emirates, Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, Jamaica, Mexico, Panama, Paraguay, Peru, Puerto Rico, Uruguay, Venezuela, South Africa, Trinidad and Tobago, Armenia, Azerbaijan, Belarus, Bulgaria, Croatia, Czech Republic, Georgia, Estonia, Hungary, Kazakhstan, Kyrgyz Republic, Latvia, Lithuania, Macedonia, Moldova, Poland, Romania, Russian Federation, Slovak Republic, Slovenia, Tajikistan, Turkmenistan, Ukraine, Uzbekistan
Cluster 3: Marginalized:
China (↑), Indonesia (↑), Vietnam (↑), Bangladesh, India, Mongolia, Nepal, Papua New Guinea, Pakistan, Sri Lanka, Iran (↑), Oman (↑), Yemen, Albania (↑), El Salvador (↑), Guyana (↑), Honduras (↑), Guatemala, Haiti, Nicaragua, Algeria (↑), Botswana (↑), Mauritius (↑), Tunisia (↑), Zimbabwe (↑), Benin, Cameroon, Central African Republic, Congo Rep., Cote d’Ivoire, Egypt, Gabon, Ghana, Kenya, Lesotho, Madagascar, Malawi, Morocco, Mozambique, Namibia, Nigeria, Senegal, Sudan, Swaziland, Tanzania, Togo, Uganda, Zambia.
Notes: The arrows indicate those countries shifting towards the upper cluster between the beginning and the end of the period.
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Notes
1. The idea that technology is a major factor to explain cross-country differences in growth performance is also supported by a growing number of empirical works (e.g. Hall and Jones, 1999; Easterly and Levine, 2001). Overviews of the literature on technology and convergence have been presented by Fagerberg (1994), Islam (1999) and Gong and Keller (2003).
2. For instance, in previous decades, the discovery and wide diffusion of GPTs such as telephony and electricity have greatly enhanced the interrelatedness and the connections among firms, as well as their productive efficiency. More recently, innovations based on ICTs (e.g. mobile telephony, Internet) have dramatically increased the network capabilities of economic agents.
3. The multiplicative form employed in equation 3 indicates that HK and TI are assumed to have an interaction effect on absorptive capacity. To illustrate this idea, the availability of advanced human skills may increase the possibility to make a more productive use of advanced technological infrastructures such as the Internet; in turn, Internet and ICT-based networks may enhance the access to data and information, thus benefiting more educated workers.
4. In the special case where θ1 = δ1= 0, we are back to the standard formulation where it is only human capital that enters the knowledge imitation and creation functions (e.g. Benhabib and Spiegel, 1994).
5. For some of the indicators, data for a large sample of countries are only available for a slightly shorter period. The precise time span for each indicator is indicated as follows. Patents: 1985-2003; Scientific articles:
1986-2001; Internet: 1994-2004; Mobile telephony: 1993-2003; Fixed telephony: 1985-2003; Electricity consumption: 1985-2003; Tertiary enrolment ratio: 1991-2003; Higher schooling: 1980-2000; Secondary enrolment ratio: 1991-2003; Total schooling: 1980-2000; Primary enrolment ratio: 1980-2000; Literacy rate:
1990-2004.
6. In addition to those presented here, R&D would have been another useful indicator of innovative intensity.
However, it has not been used here because its country coverage is much more limited than it is the case for the other two innovation variables.
7. For each aspect considered here (tertiary, secondary and basic education), we have chosen to use two different indicators, one referring to the period 1980-2000 (source: Barro and Lee, 2001), and the other to the period 1990-2004 (source: World Bank, 2006). This makes it possible to check the robustness of our empirical results, so to ensure that they do not depend on the time span available for each indicator.
8. Relatedly, one possible disadvantage of using a large number of indicators is that some of them also reflect other aspects of the development process like consumption and investment patterns, as it is for instance the case for our indicators of fixed and mobile telephony, electricity and education levels. This limitation is, however, not easy to overcome, since indicators that try to measure a complex concept such as the one of absorptive capacity are inevitably closely related to a country’s overall level of development and, hence, to a broad set of other aspects of an economic, institutional and social nature.
9. The cluster analysis has carefully analysed the robustness of these results by experimenting with different input variables and different clustering methods. The exercise has also been repeated at the beginning and the
end of the period to assess the stability of the results over time. The three-cluster pattern that is presented here is robust to these modifications.
10.In other words, the (unconditional) convergence analysis presented in this section does not assume or test any theoretical convergence model. By contrast, in the absence of a theoretical framework suggesting how these three technological dimensions precisely evolve, our empirical analysis simply aims at measuring the direction and rate of change of their dynamics, without relating them to any other possible explanatory factor.
11. The notion of σ-convergence has increasingly been used in applied growth theory in recent years, and it has constituted the basis for developing new methods for analysing the evolution of the world distribution of income over time (Quah, 1996a, 1996b, 1996c).
12. Note that this result is not affected by the fact that a few fast catching up economies (e.g. China, Asian NICs) have passed from the lower to the upper cluster over time. In the analysis of cluster convergence, in fact, these few dynamic shifting-cluster economies (listed in Appendix 1) have not been considered as part of the upper cluster. If they had been included in the latter, they would have decreased the centre of the lower cluster and, hence, they would have biased the evidence in favour of divergence between the two clubs. In other words, the analysis of cluster convergence requires that the cluster composition must be held constant over the investigation period.
13. This result is consistent with the recent empirical study of Feyrer (2008), which has identified a close relationship between the evolution of the world income distribution and the dynamics of TFP.
14. For a related growth accounting exercise in a standard cross-country OLS framework, see Benhabib and Spiegel (1994). For more general presentations and discussions of the growth accounting methodology, see Easterly and Levine (2001) and Caselli (2005).
15. Note that since the ß coefficient is usually expected to be negative (in the presence of cross-country convergence), when the growth equation is specified as in equation (14) the estimated coefficient is instead expected to be positive (and smaller than 1, in the presence of convergence). This expectation is also in line with the stationarity condition (see the panel unit root tests reported in the bottom part of table 6).
16. Recall in fact the parameter definitions previously set out in section 2: a = (θ1 + δ1); b = (θ2 + δ2).
17. It should be noticed that, while the advantage of the panel dataset is to exploit the time series variation in the cross-country dataset, the common disadvantage is that most of the variables are only available in panel form for a somewhat more limited sample of countries. The panel regression results presented here do in fact refer to a sample of around 70 economies, which is smaller than the whole cross-country sample that was used in the previous sections.
18. The indicators of ICT infrastructures considered in previous sections, mobile telephony and Internet, are available only for a shorter time span and cannot be used in a panel setting as the other variables. We have therefore not been able to consider these ICT-related variables in the panel estimations.